Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls
Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls
复制标题
应用长短期记忆对带电铁电畴壁的电导贡献进行去卷积
DOI：
10.1038/s41524-020-00426-z
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发表时间：
2020
影响因子：
9.7
通讯作者：
Grande, Tor
中科院分区：
文献类型：
--
作者：
Holstad, Theodor S.;Ræder, Trygve M.;Evans, Donald M.;Småbråten, Didirk R.;Krohns, Stephan;Schaab, Jakob;Yan, Zewu;Bourret, Edith;van Helvoort, Antonius T.;Grande, Tor
铁电畴壁是一种很有前途的准二维结构，可以用于电子元件的小型化和纳米级控制电子信号的新机制。尽管在实验和理论方面取得了重大进展，但大多数对铁电畴壁的研究仍然处于基础水平，并且可靠地表征紧急输运现象仍然是一项具有挑战性的任务。本文以六方（Er0.99,Zr0.01） mno3中带电畴壁记录的数据为例，应用基于神经网络的方法对局部i (V)光谱测量进行正则化，并改进信息提取。使用稀疏长短期记忆自编码器，我们将竞争电导率信号从空间和电压函数中分离出来，与电导率图的标准评估相比，有助于减少偏差，不受约束和更准确的分析。基于神经网络的分析使我们能够分离与尖端样品接触相关的外在信号，并将它们与（Er0.99,Zr0.01）MnO3中与铁电畴壁相关的本征输运行为分离开来。我们的工作将机器学习辅助扫描探针显微镜研究扩展到局部电导测量领域，改进了物理传导机制的提取和干扰电流信号的分离。
Ferroelectric domain walls are promising quasi-2D structures that can be leveraged for miniaturization of electronics components and new mechanisms to control electronic signals at the nanoscale. Despite the significant progress in experiment and theory, however, most investigations on ferroelectric domain walls are still on a fundamental level, and reliable characterization of emergent transport phenomena remains a challenging task. Here, we apply a neural-network-based approach to regularize localI(V)-spectroscopy measurements and improve the information extraction, using data recorded at charged domain walls in hexagonal (Er0.99,Zr0.01)MnO3as an instructive example. Using a sparse long short-term memory autoencoder, we disentangle competing conductivity signals both spatially and as a function of voltage, facilitating a less biased, unconstrained and more accurate analysis compared to a standard evaluation of conductance maps. The neural-network-based analysis allows us to isolate extrinsic signals that relate to the tip-sample contact and separating them from the intrinsic transport behavior associated with the ferroelectric domain walls in (Er0.99,Zr0.01)MnO3. Our work expands machine-learning-assisted scanning probe microscopy studies into the realm of local conductance measurements, improving the extraction of physical conduction mechanisms and separation of interfering current signals.
影响因子：
10.8
作者：
Schultheiß J;Lysne E;Puntigam L;Schaab J;Bourret E;Yan Z;Krohns S;Meier D
通讯作者：
Meier D
影响因子：
17.1
作者：
Rashidi, Mohammad;Wolkow, Robert A.
通讯作者：
Wolkow, Robert A.
影响因子：
41.2
作者：
Seidel, J.;Martin, L. W.;Ramesh, R.
通讯作者：
Ramesh, R.